
Transformers [1]}, originally proposed for sequence-to-sequence translation tasks,
have shown promising performance in many vision tasks such as object detection [2]}, [3]}, image/video segmentation [4]}, [5]}, [6]} and tracking [7]}.
DETR [2]} reformulates object detection as a direct set prediction problem with Transformers which is free from many hand-crafted components, e.g.  anchor generation and non-maximum suppression.
LETR [9]} extends DETR by adopting Transformers to predict a set of line segments.
PlaneTR [10]} follows a similar paradigm
for plane detection and reconstruction.
These works show the promising potential of Transformers for structured reconstruction without heuristic designs.
Our work goes beyond these initial steps and asks the question:
Can we leverage Transformers for structured polygon generation?
Different from predicting primitive shapes that can be represented by a fixed number of parameters (e.g. , bounding boxes, lines, planes), polygons are more challenging due to the arbitrary number of (ordered) vertices.While some recent works [11]}, [12]}, [13]} utilize Transformers for polygon generation in the context of instance segmentation or text spotting, there are two essential differences:
(1) They assume a fixed number of polygon vertices, which is not suitable for floorplans.
This results in over redundant vertices for simple shapes and insufficient vertices for complex shapes.
Instead, our goal is to generate polygons that match the target shape with the correct number of vertices.
(2) They rely on bounding box detection as instance initialization,
while our single-stage method directly generates multiple polygons in parallel.
<FIGURE>